The present invention relates to image analysis and, more particularly, to methods and apparatus for analyzing and improving the quality of images obtained, e.g., during echocardiography.
Imaging is recognized as the most commonly used tool in medical diagnostics. Due to its non-invasive nature, imaging is the preferred procedure prior to any invasive treatment or analysis. Medical imaging techniques include Magnetic Resonance Imaging, X-ray imaging, gamma imaging, ultrasound imaging and the like. In Magnetic Resonance Imaging, magnetic fields interact with the spins of the atoms in a tissue and the interaction results are monitored and analyzed to provide an image of the tissue. In X-ray imaging, X-ray radiation is applied to the body and different absorption and transmission characteristic of different tissues generate an image thereof. In gamma imaging, a radioactive isotope is injected to, inhaled by or ingested by a patient. The isotope is chosen based on bio-kinetic properties that cause preferential uptake by different tissues. Radiation emitted by the radioactive isotope is detected by radiation detectors outside the body, giving its spatial uptake distribution within the body. In ultrasound imaging, high frequency pulsed and continuous sound waves are applied to the body and the reflected sound waves are used to develop images of internal organs and the vascular system. The sound waves are generated and recorded by transducers or probes that are either passed over or inserted into the body. The resulting images can be viewed immediately on a video display or can be recorded for later evaluation in a single image or a cine-loop format.
Diagnostic ultrasound imaging is presently a preferred imaging modality in many medical fields, such as radiology, cardiology and gynecology. Cardiologists and other medical practitioners use cardiac ultrasound imaging, also termed echocardiography, to evaluate the condition of the heart. Echocardiography has the advantage of being a non-invasive, quick, inexpensive, convenient and safe diagnostic procedure, and is therefore practiced in many hospitals as well as private clinics.
The primary drawback of echocardiography is the difficulty of acquiring good quality images in patients with poor acoustic windows. Moreover, clutter and poor resolution can compromise the clinical utility of images of any patient produced by even the most sophisticated ultrasound scanners. With echocardiography, the difficulty of acquiring acceptable images is further compounded by the fact that the region of interest, the heart, has complex motion patterns. The advantages of echocardiography procedure on the one hand, and the unsatisfactory image quality on the other hand, have led researches to apply image processing techniques so as to at least partially improve the echocardiograph image.
One image processing technique is “thresholding,” in which one or more parameters (typically intensity thresholds), are used to generate an output image. For example, in one implementation of the thresholding procedure, the intensity of each to pixel in the original image is compared with a single intensity threshold. The original image is mapped onto a binary image in which each pixel has one of two polarities (say “0” and “1”) depending whether the intensity of the corresponding pixel in the original image is higher or lower than the intensity threshold.
Image processing oftentimes involves mathematical operations performed on histograms characterizing the images. In image processing context, a histogram typically refers to a graph showing the number of pixels in an image at each different intensity value found in that image. For example, for an 8-bit grey-scale image, there are 28=256 different possible intensity values, and the histogram graphically displays 256 numbers showing the distribution of pixels amongst those intensity values.
A well-known mathematical operation is “histogram equalization” (see, e.g., Eltoft et al. “Real-Time Image Enhancement in Two-Dimensional Echocardiography”, Computers in Cardiology 1984:481). This technique is based on the assumption that images embodying the maximal possible intensity range display optimal contrasts. In conventional histogram equalization, an intensity transformation, also known as a Brightness Transfer Function (BTF), is used to increase the spread of the intensity histogram characterizing the image. Histogram equalization can also be combined with thresholding and/or intensity transformation [Gonzalez R. C. and Woods R. W., “Digital Image Processing” Addison-Wesley, pp. 166-171, 1992]. However, the results obtained using the above techniques are far from being satisfactory. In particular, echocardiograph images processed using prior art techniques suffer from poor resolution and a substantial amount of noise.
The major artifact in ultrasound images is clutter, which includes irrelevant information that appears in the imaging plane, obstructing the data of interest. There are several causes for the appearance of clutter in an ultrasonic image. A first cause is effective imaging of off-axis objects, primarily due to highly reflective objects in the transducer's sidelobes (e.g., the ribcage and the lungs). A second cause is known as multi-path or reverberations. Due to the geometry of the scanned tissue with respect to the transducer, and the local reflective characteristics of the tissue, a substantial amount of the transmitted energy is bounced back and forth in the tissue before reaching the transducer. As a result, the signal measured at a specific range-gate includes contributions from incorrect ranges, in addition to the relevant range swath.
A known method for handling clutter, in particular in ultrasound images of patients with low echogenicity is “contrast echocardiography” [Krishna et al., “Subharmonic Generation from Ultrasonic Contrast Agents,” Physics in Medicine and Biology, 44:681, 1999]. In contrast echocardiography the backscatter from blood is enhanced to improve its delineation from the surrounding tissue. Another imaging method known to reduce clutter is harmonic imaging [Spencer et al., “Use of Harmonic Imaging without Echocardiographic Contrast to Improve Two-Dimensional Image Quality,” American Journal of Cardiology, 82:794, 1998]. In harmonic imaging, the ultrasound waves are transmitted at one frequency and receiving at twice the transmitted frequency. These techniques however provide less than optimal results. Additionally, being based on adapting the data acquisition process, these techniques cannot be applied to all types of echocardiograph images.
Several clutter rejection algorithms have been specifically developed for color-Doppler flow images in which effects of slow-moving objects are suppressed assuming that the blood flow velocity is much higher than the motion velocity of the surrounding tissue (to this end see, e.g., Herment et al, “Improved Estimation of Low Velocities in Color Doppler Imaging by Adapting the Mean Frequency Estimator to the Clutter Rejection Filter,” IEEE Transactions on Biomedical Engineering, 43:919, 1996; Bjaerum et al., “Clutter filters adapted to tissue motion in ultrasound color flow imaging,” IEEE Transactions on Ultrasonics Ferroelectrics & Frequency Control, 49, 6:693, 2002; Cloutier et al., “A new clutter rejection algorithm for Doppler ultrasound,” IEEE Transactions on Medical Imaging, 22, 4:530, 2003; and Yoo et al., “Adaptive Clutter Filtering for Ultrasound Color Flow Imaging,” Ultrasound in Medicine and Biology, 29, 9:1311, 2003).
It is recognized that the effectiveness in diagnostic imaging depends on the ability to accurately recognize the imaged organs. For example, in echocardiography, the determination of the location of the cardiac muscle within the scanned plane, and specifically of the Left Ventricle (LV), is of great importance. Information about the LV outlines as a function of time enables automatic extraction of rich local quantitative functional information.
However, with the present signal-to-noise ratio and substantial amount of clutter in echocardiograph images, visual as well as automatic determination of the LV outlines is rather difficult. An inherent problem with automatic determination of the LV outlines is the complex motion of the Mitral Valve and the Papillary Muscles, which further increase the computational load. An additional problem is the significant variations between different patients and different measurements of the same patient. Several attempts have been made to develop algorithms for automatic detection of the LV outlines [U.S. Pat. Nos. 5,457,754 and 6,346,124; and Jacobs et al., “Evaluating a Robust Contour Tracker on Echocardiographic Sequences,” Medical Image Analysis, 3:63, 1999]. These attempts, however, had only limited success in border detection. For example, prior art fail to accurately detected contours outlining the outer boundaries of the LV.
Other prior art of interest include, Ohyama et at., “Automatic Left Ventricular Endocardium Detection in Echocardiograms Based on Ternary Thresholding Method”, IEEE Proceedings of 15th International Conference on Pattern Recognition 4:320-323, 2000; and Abiko Y and Ito T, Nakajima M., “Improvement on Quality of Echocardiograms”, Acoustical Imaging 23:169-176, 1997.
It will be appreciated that there is a widely recognized need for, and it would be highly advantageous to have methods and apparatus for analyzing and improving the quality of images, devoid of the above limitations.